{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Artificial Intelligence Nanodegree\n",
    "\n",
    "## Convolutional Neural Networks\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, we train an MLP to classify images from the CIFAR-10 database.\n",
    "\n",
    "### 1. Load CIFAR-10 Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "\n",
    "# load the pre-shuffled train and test data\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 2. Visualize the First 24 Training Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eUoguzTLwwnTCk6cGR5ZI8fiI7i+EeOkZQ5EuOqLL1Mo8NgfkRe+LdKlBqtLU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\nBCtFiuQh9U9HzLluQOnExfM45FEfONyfDnl21++xEPn2AD9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IXUEGVepK6sLqnF5vaXWO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\nwbTrm1ssM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/O8GLpkKMM5UEhjuR6GaRjKrAM0T0NPNaJzSL8rxOkwpEjmBYZdTQqxMsK0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bi7z+6XZ1LujLN0Tgw3o7Gc1IFxwLnkdeVKERtL0nrr0ovN+S/lcFV29P1glD+Iaa\niN3va1XQQVdcMnEezfieDIJslGT+2tzk9Y8Pdbe9w/PDInyHjUacx/nVzSLNBXAaTXVtUpIgNhkE\nFSn7XM5lsFNvX+P5Zu+B9u9dcYNeWNZ+1IB6+TgwJo7BYDAYDAaDwWAwGAwGw2OAh2Li5HlGyWRC\nDdgtczt9YxBQTeVU2ocTt0UREe6N9CTq0ot86pC2lEFQlh3GAxA/jER8rH1Kheju3eCdvbNrLAK0\n1dEwmVv3mOmwWobwa7Lb2m5r2V1o6BDEJN2pe7mk10UV3sE7c0nD6d59T068QXTyzi0WzxtNntF7\n6+VZIbNjYGmxTb/6K79I40M9nR/ssmAWhmAeu1PXDMQ+5dQDw37XhW3zuatPFWmf+xkW2erGWhe+\n3BuDUFZHWAipnOL0QTD5zAbvOl59WkVMp7Jj+bU/1zr45jck1DWcnKQiKOpCkhMRUe76EYro8a4r\nnthkciqWzWHsEOkO9MOiXCrR+fNnZ0LtrazwicxopDuxrSbv6C60te62JRx0o65iW89f4X6BYVGz\nEe+ynz6tO7Yvf493bN97S5/x3FXeIV4RIe333wGx6VU+Nfjps8ryeOXGK0REdHPnRpH21ItXiYio\nXdFTpW0RPutOdac8WOH+XgdBQW/Czztd0VMXXw5lfvb5LxRpWw0+oXjr6xpy9zjI84zS6ZQIWA6R\njNcu1H0u4byby8ouGIvA6sqi1un7N24QEdHduypwubfNu+/lBgiBy7HiBAT3YulrsbAVuns6Dnck\nHOn2rqa5MMHjrqZN5JSk0oQ6zTmE6qkVLefhkti6lz5ZpDlW4A8yFXP0VrkdNp5UEb3G8im68/2/\npJPAyz0qZwHlCYaGFSFOsHXulDRIQPRNxvoDCMe6eZpZiXU4XXBMqgHY+IHYMQwxvr7Btt2dmDWA\nCelOyvBUM5Zw5gEwq9yYnMIpyTvvM9tp+ZmrRVoqzxgAs/FQ6t2HWKSv3fg+53Hv9SKtHkGszmPC\n93wqRVUiH06TRLp3mmrZm3IqHcLUvS+HZUMQoG9LmMr8ANhbcsLrwalvKqeMY3iHFz/D4YEvSZhg\nv6KnU65mazWt9+GAbew0B2FjmedDONmvSDhUH9h1lRavC8KplvP2Xc5ve1f70XjqGBpa9gBOUE+K\n/GOxeo4Kpjuhch/WEE8/x2O3DAyTnrAqwqrahAdiRw4xKISsmQZD7of7HbXLTZlLSmUUZ8+kTNpH\n6sKIDWAWHPRd/WpaU04dvaGe3Hb63G6dA32uiyefAxNl1O9TEp+QgeZ55AUlCqFMkWNrgf11J7Kd\nqfavjpySjreVYRPL2mANwol3ulyPO8AYDMs8h0aJXpdGnHbbjZcdXac6wXS3HiEiikWU/sJTOjZq\nIqKbTfTentRRsKpsXifWWoZ1b3OF88mAJVucTpf1uiAIZsbUcZHnOaXQP/MiGACIqDsmDDB23Gl3\nDnUxnXIddzpa9qYIvvb7ykAajrlelheVgfRBkWYMZDAc81ywcVrX/cttrseopvaiVOX+kZH2R7cW\nzGIUqudnZRlQQIW92FrQdnRsnzCEMN2PQteYiF0XwH7kzp6h+SlYMvHsfUSUQ1j43LVLBUKRi/B7\nPsHgJiJGDVR1N1enff5WWgQW1X9V5bk13VB2wPsdtlUJqV14+hR/D/3yqgarKVVl/KJArrRpDt+I\nFAiTH9rHcyyiWJkK3slJroyc1x0zTC+p5zLMfc57BPu8gw/hot2vMawrvv8qC9Hevafz1to6r0uD\nko7hd97ndZwTm69CoJOCQQbUsJ4EsUAmpPNSODhQW+NEyaF7UavJ4zUCkex98WLBsUY5P28M/Yu8\npFiDHBeBH1Ct0aJd8JTpi7h/e2G5SFtc4L7WbOk4vC/uBkP4xnRz27kz2jejHbY/cQIMfVlb5qHa\niUZd3nHEc0GlqvVeKfEY6QB7vt/jPEoQCt0Xcf0yOiSI/YsCZO5x+Q462pc7Ax4U/SGsieS7fA2E\np3Wu/ngwJo7BYDAYDAaDwWAwGAwGw2MA28QxGAwGg8FgMBgMBoPBYHgM8FDuVETM+qsBNd5R9ZA2\n6Si+IdCmay2mCy0tqCtJTVwgDoEGGYt4XwCClT2hiS2fUVpl1GSXkxdfZNed6Q+UGhaLUN/KstK1\n8oCpTDWIJe/obBmIn4XisoH06oq4sDx55Yki7fW/vk1ERI2a5ufeO811b2xhoU3+CemvnudTEFVo\ndWOjSPvFX/h5IiLqj5QeeGPrHhERTYDi5wtPs1VXoa7nn2I3ql//pa8UaeeucNqU9LqavHcaK71r\n+5CpY1Oh843A1SoQBcBz57SehkJr235wpUjrCDVzNBrDvUKJBdcBp6aLwo/OPSwGobBEfs8TSLx2\n6/fpuIhKEW2eWqe7d+8UaZMJt20dXApJxLWXF7UvHHY78j7ajxKhLz55ScULd0Ws+8G2PsMTQeEH\ne0pZfl7cKJbbXBed5ELxt6nH46gBNL1QqHuTfa2TgdAJ/aq2WXefqZ2dAxVbfqrOrjol0uffu3aL\nfwBV/XyLx/fh298p0jYXHtqkzMVkNKZrb7xBUxCTy4Te3O2DQJ4QWzv7Wv5uhymmNaA8emKLULjx\ncJcFQquxtpETzH2wo+Khe0KJHIibVBf+1hP3A4qULru0ynTxUaqc4kjKfnigdnJnm127njqv9PIX\nf+LTRET0/l19n3tvcBstXXi2SCsvsJtSCSihvh/QURLwwyHyyrThXyrGIxFRGLItRntekr8HuV53\n0GIqarVyrUjb2GSbVYp0DA9HbEPKZS3tRNzm+n0QNZQ/n15liu1PXFWx+3fHbNtvDtQODHe5zkqB\n1vulVXHJLCkl9gfvc38d9LQtGt4lIiKKMxDP74v9WdT83t1lN6pyXcsegzfM8eFR6Pszpypx4b6i\n7zgQcesEqMPu59aeUquvbIhgZ0vnjJ0DroOap3NrIC5OL31JXTEvP8P9bCBC0bmndqAl149BfDKT\nvzebav/ClG17igLI4jqx2NA5xlHEBz2dY969K255+/qMVOw+uiZ72Ul7+yzmUehR1DURUcUAlk2J\nCICje0lNuNbr558s0rqH3DeboNN5eonr4f17ak92hGp+9iyva0J43/1dppe36zqWNsRNp1HRMlXL\n4uI+0j5fkfHqgTjmQVHn2kb1OhcwADFZ564cgutirVajIAB1/WMgSzPqD/oUg+j4SOacfgJrQmnn\nu4fav3fEHSQHF6uSuCk9gAAQt99mcekcRFo9ET7PA22zTNaMVZkfxmDX0ojzData74sbLHaZgCvG\n1i7T9NeamrZw+gLnUVF77tp0BVyA336X3Y9Xn1AXz9UmU/J9sJ2eH8wqwh8DORFN85ymcwIEoOu6\ncyOecYkU90ucaCaSD3rLHIjLxlvvvVKkrZ3i98H3zsWN4/CwI89UO5BLsIv1DQ0U8tyz7IL8xhtv\nFmmvvfq3RET09FUViHXCr+gK795ie1v77fIyu301W+pOdCjuKxGMC3SBPxk8ytGtyfm+oIuVBM3I\nQb3VcxIVDV1nOJ8OXPc6ZXJ0mfWk/VCYOy+LaL58K+QDtUFByHY8bKnLyuVFbrP/LlR3+tC5x4J8\nQz7mZ/jh0UAZVNf8XB9G0e5c/u7hLAgBHk6CLMtoOBpp/yWitrjmoV3r946KRbsx0QJB7ExszASE\nvrf3uA7vPlCX/dI73F7OdhMRrYn7/I1bvOafTjHQCNfL4rJ+666t8e8d6LdlWYu121qnHVmnTkEI\nOBc3vJT0GQOZA2JwWXVuTE1w8ZxORzPulcdBrV6nT730ErVXbxRp115nm7wAY6ouLqOdQx3/Q/dt\nC/N8SVxrp5m2Tz0Q4f26lnUoa43I17VGs8b3uvVM6Ov4CisiVA/u9Enk3J80bSwi3RGsXfsTtlOt\npvaPScx/v3dP2+z6DZZ8WQZX9GevsCtiAPndh++LjwNj4hgMBoPBYDAYDAaDwWAwPAZ4OGFjIkp8\nohR2jUM5sSmB0s9ERCorEKZxaY1PTitAtAjkFCOH8LxVYX8EsK3vWBdnLuip4o0LvDPcXudnXH1R\nRXprdc6jCWHihhL2cQonN6k8w4PdulSYE6OBnvY7Rkq1oScipy7y88+d11OCe3d4p21nF+7dqM+c\nBhwHnu9RVCtTBKFmL17i9/0vf10ZIU4ocasDwnrCFDi/qXV39SIzQdZXdbc3jXj33SNtR1/adAIx\ny12YRsdySjOtu10RopxMtI4TOSWYYHhQYec4wS4iokzCC8YTONaW9olmmDj8fBdqlwvlYoDCjm00\nK5h3HERRRGfPnqYUTgrfeYcFrQ8zCBkq7K0mnJCWHGsETj7feIdP26o1vW5lgdsvhjGwvsJtMU00\nrdFgEbkrz3I/7sPx//V93u09ONBd389/gu/9Ulv759f+lE+strpa71/5JT75W6jodXU5OWktqsjY\n+wucdvumnoz8g1+WU5mxmpHOBNrlBMizjNLRmAYQIjlscF1VQOBtIrvkuzsqJnd4wGV8ZaSnn0ub\nXH+DAZzui6jerRsaanZP2Bw3r2ta2BS2i+z+93s6vp04ZB0E06t1rrdBTU81xjImxiA42DngtPdz\nLedbt3kX/vaBGsqpz2Ottgah7gN+ng+nSHxidLJT2lq1Tp+6+vkZxkNJRPaiEAQUHQOqq3XRqLEt\nWF7TE4mpEzWEUJVL+QKUl+EEB3GsjaUvVT3u6zGIt45K/CwgiVDiy7yzrPXu1ZjFVGrovd2A+//1\n/XtFWnnAZRp0VSA/FEHfEE6HJyn3N98D+3LC0yrOj6gU+TNi70WNQfaJMENyYHumOfcBx+IgIrq5\nz9c9eUbZS08/zWN8eVWZXwcinHv+ol7XE1ZUKELSpYr27ZsiDN7vqv0jYdI2Qf0zHvNYGYJobxiK\nAH1D6+5QxsA9YBG98hafUO71QNVSBEB97N8nDHGN8H1/holTzDNwWlnMOYE+13eioRA22dmEBoRy\nv3yF2U2vv6wCu9v3uP9d3tCQ2E8/ye3Qnzrxb6g/EV+tgLBxKOWsQCjehoQlX1rQuTmRsq/DKe2B\nhJh9AOLAO/vcv9GMT8bcl8aenub24ynF6clsfaVSpavPPE97wPR9Uxip24c6l01FPfogQ+Yuv3ez\nomM9lXertLU+N07xWmcVmDW52JgeqKbmws7ZFaHQQxh0Y5lvljZ13Dz71GUiItraVVbrjTtc9pan\n9X56icfcZFfbsR5w+zQ8ZX/sdvh9cwjGsX72LBERlaCfh2E0sx46HnLKKacMbLIv6/gZEWP/6BrK\n9SO8N5K5KCyr7X7jtb8hIqJmXfvlmU1eLwyRgU2O+c7/1oDZPh4xs7Ne13kvzbhM585fKtK+98rL\nRET07W98q0j73E9+jssGArEu4MrtWxogYOPUpjxXbdwdGZcTGHt+8OiYOIjMjccR2LoB108ewLWL\n3C+86GjbzzSTY93AdbmkzYSLrspcLqK63kjXRbnMMRlkHAjzI4S+lwkLMQWxeccl9WAez8oioF7V\n9SQJgx+msULAnNpqNwnG90ngeR6FYViIGRNBWHuw3Y6Bg+PArU0wJHcR3h0YLnnOvwP49tiWb7IK\niJP3hty+vqw1Q3jW7j5ff3dL2RjpnF+nTvN6tgcM1kTYQcjMioXlU4Fv16aICPcGOueG4kWBovmN\nxjIF4V06CfwwotrqafrsBfXOWFnh9eyNt98q0jp9HmsQHZ16IrifgNdFyec6CKAultrC8AVPiOmU\n05YX1RvncJ/tSd8JB+eabySvvbmqc0elzNf5vo790YifOxiqDfMCYb5OtPDX32c2eg++/yolfkij\nqXVcrso6aay2JoE9io8DY+IYDAaDwWAwGAwGg8FgMDwGsE0cg8FgMBgMBoPBYDAYDIbHAA/lTuX5\nPgXVEg1TpaSVhdbaaCuFNBAx3RiEPT1xBRr2lLZXz5jiVVGWPlEsVDOg/q0tMeUvAUGgq59md6JA\nWGJPLJ4t/nZrhynxnQON+R6V+cIYhFITEV+slcGdSlxYmlUQLJay1Ota0NOXVomI6NxldUnqigtW\nF0T0hqPRrPDYMeD5PpVLpRkmZkmoiWcuqtDYk8+z6BswSOn6e+zC02wpJXWxIe8R6oUloVdOwXXK\nUVEnE33vRp3bti1C1QlQqg+lvpG2GYlLVm+k9X5LhBz7HW2fqbhY5YnSzxw9EeHojkjnzefULwqz\nHhe+71OtWqXLT6pA5aJQtW/euFGkjcXl52JD2yL3ub+98Za65ex3+B3/5vuvF2nPPcv0+fUVpfHV\nfKZe3gNK5f/1Oz/gZ1zg9/qNX1URvzdvcMd49111bXnxGa6nZz+hNM5f+/IFIiKaJkoPbC5y2/7V\nN28VaTuHTO27dFrb/Vd+/pNERDQ41PbxK2wH3v6hulh1hidzHXQISyVaOHWWDm9q/S20mRp5+pS6\nBh6KK1kOtuZmxr/fe0vreUVcoqogQJo7OwUufM069+uopHV07gy3q9NSe7evtNpUaMh+oO89GrNd\n8cDlxhNXAHg8ff9ddgELIh0HiScCzFW1K1Un8g6uUyQuNL6H7lTRXHHWh0EQhLS4tDgjAO3cngIf\nxBKF4j8B959IaMQ1T21ntcr9D8XJXd4TsMUjEdaME23HNOZ6DkO+9x5pmxzI76SrrmhTcc/MN7Te\npw3ur6MQ2kyE8CpAVw+Jy9nd1uvqMme1QMTccwxksHv+I+jyaZpSr3NA9abSf51ZQ2H8JHa2Th86\nlTTf1zp+9T22HcNM6+J8lUX0/vb6G0Wacy34uf9Y7cTly+wqEovo6L/7N39R/O2V77FLZgTCt1Vx\no2o3NY+BiNfHINoYBJxfuYziidwWd7Z1DOx2pC/A+HEiz4GPIp70SJDnTJnPP8Lt2f11DO7CqdCr\ny+Di6csYjGEs1tbYzj/9Sc1vmnydiIi2br9XpC0Jnb1S4j53+ED7d1nGYQPEOVNxfdjbVxvcH3I/\naDbVfaHR4DHZnOP22WyoC8/SCr/bfkdt4qG43O0d6DO6vR5RfrJ1TaNRp89//idpEut68nNSt92p\nPn8k7zgFseOOiJKPwU27LGu2RkPXOg3pQ95A22ws7jx5RftXN2Y78f59WTvC+eZ9WbO2FzTflRr/\nvrF3o0g7K+Kgz2+cKtKev8Dr1OilnyrSqtKJPGjCAAAgAElEQVQvSuBum4ktWoEAFJtN/l2BBV2l\nWqXaCUV20zSl3uH+TJ/1sqT4W3GdGKAknrcO099VEcK99u7LRVr3kNedL1z4cpEWEddPEGIACunv\n0qfTTNt474DrfXVF3dhymQOrII7+2c99noiIbt7UNYxzg6xDIJOhuF7fB4HYcxcu8DNWV4u05Xvs\nMrqzp/391CkNRnF8eESeT14Oc7QTb++DzsQqv5vf1rI7wdsZF1Jx6/TAvbPotuh1WvwN3IZkvZKX\nxZ0NBLo9GWc+iMe6nxm4+PvOuxTmAncHuvsW85c3xxU2BhsiQrN54B+97qTw+DsqxO8CmY9iLIOI\n6JZKPiSJeD2U34kh4xrGrSfLoX8krVpXu1uRb9GO2JVmQ+vePSuBdZUnn+mrm+pmNhLReny+LzZk\nCt9QzvUSNK0pELf45oLOD3nEbd4Z6DxMoxIl4BZ3HBx2evQH/+Zr9KUvfbpIWzvFLqbfe1ntxXjK\nZT77xNNFWj3l934H1vEtsdlVWArX6vyflWW1CbEIdlcruiba35X1vkSCSGAxHsgacx2DIYkbVxfq\npCTfzH2wiXWZCw63VNKhc8i2A7uym4PL0N4dJygP7bMEguIfB8bEMRgMBoPBYDAYDAaDwWB4DPBw\ndAWPRbQmY90tT0R8KAVh40DCXXqwvRSIclBY012msZy6lnAXWJg9QXpUYM2LdGf4qedFiNGdHCR6\n/TBnZoQ31ee3W7wTugdiZfGU8/Ph9CGQnccowKrh65xgMhFRXUI9r6zr6czpsyzoiSG+y96JI0KS\n73lUKpUoAkHpscfPQCGoeMzvUYOQbFEoZfa0jssinhdVYQfYvW/wgXCWNBtuz4WJnYjyYZyguCHn\nUQEhzEQEqGZFTN09Wk53Op9lsIstbIMM2qfQMJ4jcInlTJOTiS4SsWhlOapSCKFIz5/j3dSz5/Vk\nxoUZRNG0T3ySd5YvnH21SHv5FT4Bv/tAhSSvXeeTvwh27yuhiBd3tXGv3eL8tnrcZj9xXZ/VkwOj\nPNY87m5zPx9/W3fSXfjEHggLXoiYvfUzP/fZIs0J4b37jobx/B/+Rw7VXgWG2hNX+MS+cwB9K9Kd\n7BPB8ymo1qkCJ25OQNaxO4iIYjlN/oOv/p6miUjYoKMnEjfe41O6GJgrBxLWFEM8ptL/ak3Y1Zd2\nTaXPlcvav6euLjM4GhBxcA/GUiJjI4v03k7C17VqEHK5IgKGICqYFdv5R0+ogplTuJOdjBOpAOAQ\nwiq606sGMBEd4ycCEb+qMFbC+KjIKzJx3DhFpl1J2EY+vrd0tTDg+t8hFFXkMoWeCvsFIrCbgwBr\nEnD7xB4ILcpJjBNmJCKayOnVlPS9qwH3sxhO+50QJ9ofFJc+LpJkSrvb9ygnzatUc3MLtjs/N4Oj\ntUxOPVOw8Qcj/vvLb6ow4bfkN9riUE7vXuyqjVsecPv80R//OyIieu0HKkAYy5waeHBKK2LPnq9i\nx6kLiw6nzp6oUI/Hyhh04WSR0ZmSBDzwwe7LfIcUgEcVYDyJY7p//37BBiPS0OLLcDI3FhFSrPtI\nBERRSN+JEVfraitPnWKmcOvclSLtBRm7S3Ai+t41trnlOtffhdPKQjjsMUsMTxc9n+sqA6HHkdiz\n+yAWHRS28OgcOYVT37G8NwrtBzJ2lhvKAFmpleguiPoeBx7lFHnpzPusL4pgeYDrALE1wJzLhG2J\n649YrvPnhHDG1YBjEfowrhxzY+hYP/C3WIS+CeZ3Zzu+fEFZuo6xfQEYO2t1ZthWy/qOkVD3MHy9\nCxcdItFMyrRzoGysr//pt6l7eLKwy0kc0+72FjVhjtveZuaeD4vVhUUu+96ePt+xnmt1ZW811tnu\nD0baH8pl7vvVagvudWwSLYsn4tKJsPuHIBjalRDbp87qnJkWQsgoysx1e+mStoVjFOGYHgtD9uy5\nC1rOGr9Hf6hj4OwFEU0GW4PhnY8Nj3gNk0D4aidAvKn1SW6ceUf78Qwb141lYMwUZcbQ3cLu8JBo\nKHOaC/HtoYiyY0rCN5An64FgoDbehS7PMwgk4L7VoJFzxxbBbyq3jgQ7kztGXoKW/dFYea76nJog\n9u7E8jt7Oh/5wuiegLJ7lvF1SNjxpM5TZAIKs63f1z7swn13IZjIVFhnzkPjAALR1GUNlYzVNjjh\n44NdZYQ40WtkFo1l3VsHO+3mp05Xy7R1n/syDE1qrEq7lTW/ySD+SHbqR2E4GNIPvvMKrULQg2zK\n3zd3b2gQiYvPfYaIiFY21KNmV8KNb++ovVuQ764o0nKmwhwrRSAUv8Nterur31qRhL0fiFeIB/0x\nGUvACtLrHX2pDt/Jw6kLeKDtPhTvhO0dbZ/DA35+uaLPOHuB5/L2iu6BpLKXUoO1wv7ew82rxsQx\nGAwGg8FgMBgMBoPBYHgMYJs4BoPBYDAYDAaDwWAwGAyPAR5S/TUnyhPygJfnqOaTKdBv51C9UqH0\nxSBUNRU66QgoaamjMIEQlKOdhiDwWG4KjdipPybKdTvzBAufVqpKYXUs/WodqGaiqDwaKn0pkbKE\nvtKbfCm7HyhleuMU06xrNaVaPXGJqWDbO0q9LEf+DEX1OMjJo5xCCoD+FZT4uaCRREOhIXpA915a\n4XKG4LIRSb0grX4irjgJlDUXGjWwPylzoo250DFDoHzK71kCHufraH1ERL4TrATxM9enkL3n6LfO\nrYqIyCMRGaMPd5c6qcCrZEJeGFJI2u6uLTN4y1DcActl7cfOpexLX/xSkfbsVVa1vHbzZpH23e98\ni4iIdrdVDNsJhTaa2lfPXmTXpTu3WDzrn/w3f1z8bSh0+AzqyReBQBQqnEhD+qH27Z/+WaYgbmwq\n3bLfZ3rnu9fUnerl79wgIqJPf1oFlVsbIr4L7RiF6hpwInjsibGyqmJuFRH9zEj7dyrN8NoP1eXD\nia42ampD/vw73yMioo3TZ/QRcwTexhMZ/121Cd0B/3bCrBEITTqa8xTEtSOhPEdtLfvZM08QEdHy\neRVuW1ja5OtQnNiJjYOIsPMwxHHlOzsKNjEl78Ts4zRN6ODgYGb81IXq6YM7Zyyib+gmtSj0e/c3\nIqKkECpGVwjukzm4fTq7i64dk1DuFbtSjsFNUwSly1Vtp8CXeQLqzpfK8zMdS86txwMaeink51er\nOl6iiqPOghCn3OJDawSPwNZEYUSbG6v0YEddF1bL3C+9VJ/lhIBRLN/NCx60Tyq//ZljGjf+j7p/\n/dlfqsjgN7/DIuq7QjP3YEw7scsZd9ac2wzdn5xAYwZ1nMs9ia9t4eYgj0DIUSjtPlD8PV/mG0J3\nqpO7sRGxSP5kMpmZDx01fmdbxeUbIhT85FPqthHIPQm4ZLrfOO9XQulzkNbavEBERKfAhTBss8Dq\nrZtvExFRp6PzQqkINKAuIq02l6kK4tsNcVHoAK0/k7r3wF5Mpb2czSMiGg3UPdFhZYnXEMuLKr5f\nr5bpezd2j1z7MBiPx/T2Wz+kGgj8OvdMz8c5hW1MCC5rrt5DCM7g1psoyu7cTtFmu3s8WEP4svBo\nyAAvRZpHUOM6huFVzKso/NkR4ecSuNOH4gYYQL998w2eV//iL/6iSLv4BMsDXH7ycpE2ETv6ANaT\nBwf7M3P6ceB7HlVLEY366sbRrImLB6wdY3ExaIA7QbXCbZXj+qfG92ye13HRF5eAcl0FgxPn0ppp\n/QyGXGf37t4jIqIzpzWP557ntUYE7su551x49H0ScW3wM2jPon/oXOCEvp+5elXzk3GB9rTh+hu4\n9KHI77GRE+VJRjl8q5AT1waR7eJJOO8UbrwQ0MNVAlZGoTdwVOw4R1vsfjq3FHTLdm7gHq67xY1t\nDDZF3Ay9ZXCVdmLZGEDCuYChG12Z+1teB+FcccfzYD2ZP4p1PBGFoU+ryw0ajtW+DSeyXoCv4HrN\nCQGDDIHIVKQg8eHL+6GtKdyu4SOmLiLkGPxgJIExnBt2DG6angSWacJ3aibzIQZzGUuZsHZcH14A\nd85O5/CDRSrcUycjbaN6zM8LcpAAmAzohN5UVIkieur0GnW27hRpOzs8pyaxlr7TY1uzu6+ubXfv\nsbtVCN+9TlpjAGX3Re7i7XduF2l9ccVMYG9heUlsh+vLMGzK8r122NX+UZG1bXNxs0jbH7DNRHdb\nX/r8laufKNKef+Elvrelc9vGJs+fZZBlIelT2UQLU73r1hzfo48DY+IYDAaDwWAwGAwGg8FgMDwG\neDgmTp5TmsSUp0jNEAExEDt2244+7Aa7nXEUMOyLKOiMwJtk3RyDcFqNT4LrcLLuTlPGcupRhpOW\nWE5H0kzzlYM8qkIY1LrHO23jkVaDKwueOpck3LAH1XXuIodJw1ORapPLt1nREMgE4pnHhUce+V6Z\nEqDEhCJOXK1oPeXuHUEA2u1EpqHu7Cay6x7ATrsc8FKMW68ilDqGMHYl2XX0XXhDDLsnW9p4SuPa\nvd1WMScX/nvmhPvIWytmN+PlRBH6lgql5kfSToI8J4qTfOb5TlBrRjxRLvDhpDASlketAowGCX26\nvLZUpK0t8e7sK3+tu65x5oQxNb8bN3mX+a3XmYkzhfefiJDrONZT20BCC0cz/A05zYaDyv/vX/2B\nvCyKTHtSdr3w7FkOm+rC5BERTeUZ5QoIfyUQKvMEyPKcJkk8E9bViUMm0G8cq+Dv/OIvFmldCXV/\nC0KOrm8yA+f8E3rS9+Y1Du07GIFgpYidp8BqSuR00rFkzl64WPytL6dHeVnHV22ZGUoLy7qDvyxh\nUkM4VQzkxAvHoedYEnASneaO4Ya2RBhpuZ40RL73CGQAPfK9gFptZe45+4cMGxfact6pcAhMDxUs\n17IniROILtEHgfk5lo8n4yrugzj6gMPANtvadpGIgy7AXBDF/Dvw4Pnu9BWYI+Mhn7DEvp40enJP\nkH+A7UREIbTPFEJOHx85UZZQs6n2vBB0h/nWsdDwhKkiLMcc6jjzjp7IOjZA4Ok85nI+6Go9+nJi\n7sJueiCm6U4U8bTUnQ77wIByB7vTiebrTr1LeOor+QXA6By794C+7ZgZKM7pP6rws+SR7/szc5lb\nB9Qh/Lbryz/84Q+LtIaw1DbXVIC4WnbrBUUheg+JJWH2La2fLtIGwgpuCUvCK+vzD/bZrmXAQNrZ\n5ZPWIbCJ63LSVwLxx8KOQhuNJcxwo67z0bqILmIY5LoI4HYHKvp97dZWIeh/fOSU5xn1+0eFHLEt\n3Ik1phVzLpyEO6b23G6Bgtj+0bWLY7g5xgG2u2Mi4vVOdDiBOtjfY8ZMHUKAjwY8J+Na5/4OM5gW\nVpSl4slYe7AHIX5lnJRB9PK5Fz8xw7o6LnLyi/FNpPY5ngLTQux9HZ5fhOcFpsJgwKfnPgRnaAp7\nazTRPuPEt8vAyu51HatM2KuwhirLuh/DVRdCsnPYJ3ivW/8hE8e9Y4pi686O42m/tC3al1Lp5Ky/\nPEkpOeiRD7bOL7nngmFz8yLSKHM338Pa2tVBhusCWQvDe+uqAK5zbeDWbpBH7thYyD6SgAze6nlN\nc/MTUtTEQwDXmCThsHNg2VJVAjgAk85xCjxk8QyPMgOPg0qlTM8+/QT1R1qGofy+f09DQ+fyzTiA\nADiOZFmBcPVursMQ366vRVD3rhrQdiSyRnb9q17Rb1IXujwGhp9jDqPYchTxPSlcp4xprfyhvMc0\ngW9sWZ9tLOh3am9PxikwEPMpfdCt4qHh+zk16hlVq1onjUWeY/q51vEdYeKNYvjGlf6/uqTBBZKU\n7VMFxJtdz93vquh2nnO/miLLVESmSzI/o/2bCnu1BEF52ks8p9fbOj+fqXOZzjyt3zytFjP8Lp7T\n74JMbMzBvrJVJ/FgprxERM6MnlrTZ1y+wnXwP/1vv00fB8bEMRgMBoPBYDAYDAaDwWB4DGCbOAaD\nwWAwGAwGg8FgMBgMjwEeWtjYyxIKkSsnNLHdA43lTuKO0WwpJT+Q/aK9A6U89QZMjULKoxPA7faV\nRudo4nGi1NVWm+nBY6HoJeA6lYioaw6U85IIh5V9JTOVS04kDeji4v6UgsCbyzuno9TwKYiUOeHj\nEGjMCU1OLDaa5TmNJhOqV5T+5Ql1Nayp6GRD6Ko+uAnEXa6fCtDEooq4UwG1OhR/s/FAKW6+CPqG\nAbg9ZE5gTWh/4GIxFpe6pK7Pr1Q5D6T9OdGwHLjx7ncObg/kBEjn1t/RxEftTuV5HgWl0owriHuz\nEtC4nYClN8PjlueDeGvgXNDA3e3F554jIqKzq2eLtDu77ObTH6h7VJrdJSKiy88yja9SUzphLHU2\nHCu1MxUBuihAgXB+7hDydaVbANHKJ0VccR1EhZeEjt+A51bq3H/CCF3bQKjuBPDIo8D3aTBUOnZP\nRD5xbO7vMA1zDLTtUOi9G5vqznTu4iUiIvrWd1XAdWubRWRrdbVTqbiuxLE+IywJdVXsxH5P33H1\n7LP873kVpKwtsutWqaL024JqC9RjR7vNoC87mnoOdHHnbhUCXb3V5LzPr6ub4sXNZXrtd0E07Rjw\nfZ9qtXrhVsCF+dHXI024oKvPcbFC0Vjnkojj2rmqICXeiYv6Yrtv9rXeF3ym8Nfrmm9HaMkNsGul\nrCWvoGMjp6MUbSfmnYZoQ8Sewus4Yc0p2L3h4Kg7yMMiz3OaTKdUqar7oPOe9cGWxcLtDlB0U+o2\nQ1c0mUdRWNjpBKM7VeJcEYBa7O7IcplHIQ/3E+eOXHysUOzdCYEG0N/VRUevK9xMQPnR9SkUNCz6\nFEwPvv9ozqDyPKPpdDozZzj3p2pVx5MrF7qyuetGI503nfs3il5OY1k7ANU9ztx7qk09fZGFzytC\n0b5x7ZoW1HOuzDj3cb1s76hLgKuWOggGOxsTglt1JPkF4NLg+tcOiOne3uLfOcxvg/GU0hOKvfq+\nT9VqdWZ+dcA3dPWONtEZD39mGSAuJyleN5uHZIT/EBFRJOPJuSKkIE7c63DbZfPcv8H1o1J2gsl6\nXb/Ldgqrqr3ANml5Wd3Y3LtluIaRcYJpgZ/N+hQeAzkRTZMcROdhLPm4Fpe6APejkVtXgMvszffY\nbXkfXAfOnmG3m3evqXtYJv291dL3PiPXra24a0BYeZTMlo203gNIc0KlOO+435jmxMrRncsJOeOY\nLgSNod7n9dGHRZql1O33ZkVrZb3tNXWdVgwzsH+5syXYaaVMHoyL3NULpsmaxoPvEvc7d8FacnC3\nkbqYEUIWwV0PZBFcwAECUfdCXB/W+4VBGoHbsXNDgm+/3H2jQFt4w5PPrUREURDS2uISNWpa1u1d\nXv+9cPU5TdtjMd1c/iUi8gJuowmsRV2wFew3xbyF85L7roG6dHbCzW8LTZ3znRvvEMTrc7H3wYyI\nu1svaf25wBJ9+Ha+dInlA6ap5ucEnUsl7XN7O/L9DusKL/dO7E4VJzHd2b5LYQm+VxIu80HnEK7k\nB/X2dR9hdZ3X740GfLvKtw5KpfTkfYfgAudcwOsgyu5cMRMJwjQaaT0d9rk/noXAI088/RkiImq2\nTxVpfondAPcO1a7t7fP8OBxpmUayt3Hj9r0irS5r9p09DZbgyTfF4qK6U/XhG/zjwJg4BoPBYDAY\nDAaDwWAwGAyPAbyHYSx4nrdDRDc/8kLDB3E+z/PVj75sPqzeT4Rj173V+4lgff7HA6v3Hx/M1vx4\nYH3+xwOr9x8PrN5/fDAb/+OB9fkfD6zef3z4WHX/UJs4BoPBYDAYDAaDwWAwGAyGHw/MncpgMBgM\nBoPBYDAYDAaD4TGAbeIYDAaDwWAwGAwGg8FgMDwGsE0cg8FgMBgMBoPBYDAYDIbHALaJYzAYDAaD\nwWAwGAwGg8HwGMA2cQwGg8FgMBgMBoPBYDAYHgPYJo7BYDAYDAaDwWAwGAwGw2MA28QxGAwGg8Fg\nMBgMBoPBYHgMYJs4BoPBYDAYDAaDwWAwGAyPAWwTx2AwGAwGg8FgMBgMBoPhMYBt4hgMBoPBYDAY\nDAaDwWAwPAawTRyDwWAwGAwGg8FgMBgMhscAtoljMBgMBoPBYDAYDAaDwfAYwDZxDAaDwWAwGAwG\ng8FgMBgeA9gmjsFgMBgMBoPBYDAYDAbDYwDbxDEYDAaDwWAwGAwGg8FgeAxgmzgGg8FgMBgMBoPB\nYDAYDI8Bwoe5uFSK8mqtTJTlRZpHGRERVWsVTfN4b6gURUVa5vF1aZoWacmEf2dpRnAhERFNpkmR\nNJqOiYgo8HXPKQi46GHAadXK0ecn8Cz33CTVfOUy8j3N1yN+PuWeFkneNwiDIi2K+HejVSvS/Ion\nt+r7eL5HD+7sU2e/rxk+JJrNer6yukAeQb17nJ3ve5Dmz/yN/340bV5BcnnHLNeyZxn/znN4ruRX\ntMXczOCn3JtDvppdfuQefFaW5x+8oXiPmfdxadA//IDb5623buzmeb46p5QfiXo5yBdq4dwy+fD8\nIJS+CP3D9/m3BxXk+fPK7tpHn5tI35toVyVf6i+S/u57UHdFZvDTZTjT7t68C+WH5ufeN02PpkEV\nF2WfV4hrt+4fu96JiELfz0uBjz1E+xI+TcqAY9OVAfuca4+ZviyvjuPf5RMEml8mtiNOEvmbXh+J\njXP5ExGlSczPyo72eeyjUanE+cN108mUPghXFh+e6/rheDzW52YZxXFCaZoe29bU6rV8Yak9U0/u\n+UkcF2lqf/S9Y/k7vo97RxzDqdjgMMTpR9oMbDZJW/iuv0K+7he2exonUiawA/L3NIF859SOuyef\n7VxzbuALplOtC/ce9+/cO3afX1hYyDdPbc6kFU/9CNt9tLx4HY71OffkH/K3D3vUvKyw8pxNnvt3\nsCtHfswr3Idf9+abb53I1tRq1Xyh1aJ8zvyKmFtF3py/FoMdL3M26egL4LOONMfHnF8/uqBz7v2w\nC+b8MZ+ZI4g63R6NRuNj25rl5eX87PlzHxh0mr9Dv98jIqJqtVqklcWuzAzXOc/4sO6N97o1qLN1\nKawT3Tqy5GzZTI6aSyI2pt/vH3mKN68E2GXEtiVJcvQ6mr1u/+CQBoPBses9jIK8XIlmCuCW9GGo\ntrNSKRMR0WQ6KdLGQ56fcjCn89Z4bv2Fc4HOGfqMUoltZ61RlrLpnODqJI31Yf0+l6VSber7yBSQ\nJTp3juXbYjrV+VFtyFE7tdRuFUm9wVDu1fzcHVmaHdvWeJ6XH7Er84bvvK7iuXXf0e8SXLMX1/lH\n12a4fghlLaprG61jVyT8tnDr/SyftQFcpqOFj0plvVfmRx/WVEk8xcv5uqJ8mui+23rd3olsfKtd\nz1fXF2YT59jpeTbRjV1vZrzKuM7h21HW7bjuc2vGZGYtKPdKhriGazTqUg693q1x/ADmiXl2/0Ne\nYmZum3NLNie/LM9pf6dH/d7o2LZmZWUlP3/+/I+abBRHzenDX3fsUgI+Yh3y8A+ZM7fB3JKOD4mI\nKKho3/RkTf233//+x+rzD7WJU62W6AtfuEL+VAdjIB8sm6fUCD7Yuk1ERC8+/7zeu7hERERRuV6k\n9XcOiIioe7hbpCXTARcs0glzp8cdOtN1M3X3eaKsVzi/J86fKf7mbK8X6gZLUOLKX1hT43/+CV40\nv//etSLt3XduERHR5aeuFGlZmSeOzSeXirRoiRtn9YKmpQ1unNFoWKT5WUb/9D/73+kkWFpq0T/7\nb/9ziiLtQKUyN10Ek15ZDGcEm2elMtdjpab1WYl4wyvK9d7xmN9xOFGDUmx4gfUqSbuUSvyMckWN\ntTN2SaoNFcec72SiCwE3OWYxLFjEKMbwoejumZlMZUy45xMRVWu8uKs3G0Vao9UmIqLPfv4f3qRj\nol0N6B99cZVSMHBjWdBUYNNwYZEH4NKS9oWWPD8KtX7cxFauadn9nH/n8DG6nfB1t7rafyd9HuyX\nl7iOn17XfLOE22wKw3kqc4BbzBARJfJtX6roGAylfL6v10WRLJ4mWqahLN6SXNvRLW4D+Bh3C92f\n/8f//Nj1TkRUCgK6vLRACR39cA/L2pfzgOuvUtW6chM/tsfGxgYREe3uPijSYlngra+pnTx75jTn\nB8/Yvr9FREQHB/tERNRsq8FdXVsnotlFi5vghz1dzI9kcyaG/er9Dn+c3Lx1q0jrDfge3FhqNbhf\nn97Uj/wNee7bb72l+XUP6e0bt+kkWFxeoH/yT39zZmFRr3N/2d/dK9KmI/57e6FdpLmPl8FI711a\n4bqdjtUmHuxsExHRwqLe215cISKi3TtbRVqwxLZ6pcFzSzwYFX/bHXLdrayv6fN3eT5pNtXG19r8\ne29Py+76aK2mH4Vus2mIm2heJH/T62KZ77a2tJyrq/yOv/Vf/7Nj9/lTp0/R7/zfvz2TNm+jbO6m\n/Jzr5n0wzVvYw9PgXvdRdnTTZd7mhrsum3NognORywcPcuYdFOhHYX7kOvwodL8/9cnPnsjWtFtN\n+o1f/3sz5fKLwwp9X3dwM68OEK5cuBEczPmo/eCzZp7rNm7hb658WE43X8491JhTTlysu4+/mU0k\n15ZQTvc8TIvjmH77X/7BkfwfBucvnKe/+vY3CntJROTJh1wGmxl/9bW/ICKiZ555pkg7c+bMTNnw\nPT6qz7vrBoNBkTYcsn1aF7v65ltvFn97+513iIjo0lNPF2lubYLrFZff++++W6S5j+VWS9fH7vk4\nNrKE63YMtnNeW2RZRr/1W7915J0eBq3FOv3c3/8JKgVq14ZiW9NMbWy9yvMcrsVjWSfGQ22zXrfL\n+bY0v7r8ThOt/6WlRSIi8kv6PqfPcNpY1oJ3798p/uYTz+nv/kDntFde5t8/9Z/8p0VaOb1HRERn\nFnW8vX2d2/Pa228Uad2RrF1gWGRT7j+/9KUvFmnf/MHrRER0+97dIm0q5Rv0h8e2NR55FAXlmQIU\nh2O4mSGbAdmcg5RaTdc5I+krE+iDdVmbLK/rWmFlg+fWZ69cLNKeOMVz1g+//7dERPTOO+9pmWQe\nWV/V9VO7we3pRbrOKZW5LL3DTpHWG9BdumUAACAASURBVPP8+cKnP1ukLciY6hzuF2nvvP59/pFo\nf3ebtJ6v68l4zP3x97/6xyey8avrLfqf/9ffmLE17rsmgOe5jTE8YCoOePBQcyxjE/Y3RnsyhgZq\nu8q+rPlXtd3u9XgNekfWfd/+9veKv33mpS8QEVGloX35/BWuv+aSrtsDj8uXwjeUezd3iEhEFMdS\nphQ3hSJ5R7U/acbtNoX8JnFK//y//3/pJDh//jx9+1vfohQ3p+fuZ//oTe7Zvx099KbC7s85sJ65\nMJ+9DogabuMNz8aLv2Zzdnbm7rTO+XsGayIZV5NrXy/S+ls8/hY++etFWiDf52Gr/bH6vLlTGQwG\ng8FgMBgMBoPBYDA8BrBNHIPBYDAYDAaDwWAwGAyGxwAP5U7lkU9h1qDFltLgo5xpWM2ycolOX2Ha\n6XJDae2DkVCJgK2+fJopf5vn1J0hnjI1MxmqK8JLVX5eyVO6ZkmollnKlLB9cM3e6zKF6ead+/qw\ngClRh73tIqmXDKW8+j6fe+kqERFdevpSkeaHfG91QelnY6FY3bmnrmC1hKtzeXG9SJsmQ/I91Ot4\neHhEFJJHAfK1hB6Xkeadh+ImUNH3abWZrlqtqQtInjDFrt/RuoiF1oi+mKFQONEXPIwcFU9ctwKk\n5Dka/FG/bqQnFporwFJz9Ol5dPN8Rk/D0dOApl/ojSDt/2g+D4s8zylJE8rQR1fKFwM9cCA+1OWy\nuliFgfh4h1qOmvOthVHnOQpkqnU8dK9Y1naMxQ/n5vYNIiI6J+4nRER1ca0LwHWrJe0yHOmAu7/L\nLllTqJokEv/cELmAXAA0DqVI9nsz0GWRtiiH6NL3aPaFc8oppXyGUlyp85iPwJUtE7cjpNU76rFz\nAyLSPrwolG4iojhhenUV3fDENa5S1rp0LjalOl/XALu20OY2Qo0UR8nd76nr1oOdvZl/iYhu3mEa\neAI+7a0lfn4OtNbdm0wx73fVJakccB3US1oX46g84zN/HPi+T5VqZcb90dH+Gw2tp3uH3JeqU7XJ\nzu0R26LIB9rRUcKdGyIR0WjENmkwVBeHqCr6FDV+LtqGD3cNUriyoJ6Gs0Uz7jrpUbcep4uRkdaF\n61voTvmh/ukfE56Ua55e1qyblEub5z6DbiRzNF0+RE9sfn1+iDsVuj+5f6F95ml7FDphMB/Ot/cy\ntwEF3GnqZf5Rd6pHhbmuN1CP89pZtZQ+vA+opthRV5957eEwT1ME59JZXamj93zw3hlNC/rRrlNY\nznnubdxXjzzmIeGR7/uUgsCKs18HHXXR2N7mdYpziSUiOnfu3JHcPswNb55r2f37uj507pY72ztE\nRHTr9q0jf9v77neLNGfrZrTT5Fm7O7quWhWX3nmuerP6bKH8C67CRb1An89Tyk8o/jAaxfT6q1tE\nCcytVafthi4JPKf1ezrvPP0suxv3R2qnF8UdudHUNdmwz+vJzmG3SLt1h+ulDK79P3yN3aMy4mcs\nLevcmiX8DVBqqP2ti5vJq9/7tr7PHru2funz6m5HHs/9fqTtU0rd2lX7dn/Kz8V2dLZrMomPpJ0E\nORHFaTaznnb9HedtN7d4c9yuauAq3BY3XtSaufLcc0REFMH6pSr1/elPPaeFGfH4Oi06Me3qU8Wf\nDsQtOQC3nHDIbYDfG2GT1z5nNnVNNZQ+tXVfPUHa68tERLTQ1vXYUpvXAEGmfcbpBWbALRgmj0Lo\nhMd/FAUUw7rKVXkAWjNBYZNRPEzkBeC7xquIFmsNtH/KvMZ4cAO+MXe4/9d9dac8d/ksZ0u85vlC\n+BPF3179/vtERLR6StdG60/ybx9ct1pVXhOhTo5bR6bw7eO+54II7LnckuA3koz7FGyNF/jz3YYe\nAgcHO/R7/+r/+IA0jPeBf4m8OZqb2q9hziSnDYVpjAjWFb5o5iJNxZP/FN+1hC5znIa6pm7PAL//\ngpLTzITvTxXX1evkGR7Mz64N1u+/VqQdynfd4T2dbwIfXdA/GsbEMRgMBoPBYDAYDAaDwWB4DPBQ\nTJwgiKjZ3KAIdn4rksN40ivSnGBtGXarqiXeQT7s65bc+7JLubCi+bUXeYfxEJg4Z0Ssa3igrIIH\nPT4dcdq8/VhPRis1ZinUm7pLurPDJyxrF1QAuSmCmdMBnE4Jc+L6D1W4crTHpw4o7Hk44rLf21Nh\nrt/8x7/G7wCntLvdXSI6GROHKKeUMvJJ36dW4bJvrKtY2alTfEqyCIKhNTnFDnwtU1/qbgt2R90u\ncwa7gE4gK810B9qxL4qTCxSJS7kuMEpA7k5TQRyq0JUiPO3j/CI8ZZRdZoxY4JgweAIZiQgtCnVh\npIRjI88pTRLyAtixlV3XeaeXuMs/EfZGDur1YxF9y0hP5coivh1WgO0kYmheAqd8ks/1e3xKEiV6\nEvaZFy4TEdEqiCcGUqY6nHo1m9wGuwc6tg73+NSgvax9ZnmRx2o+0vfp90XwECxGq8Flr1YxEgE9\nEuQ50TRNKAQB60B+exBlwR0c4EmnY3pgmhOdTFMUruX+tbOvtqu6xH3ehx353aHs/gv7bwD2YnGD\n661R17S332RRzFd/qKLDu/vcbjmI6JVrXM81OJmrSXSC9VVlJ+7d5zbqgoDgW+/yic1aW9kx0QeY\nHMfGB0gFyZyoXO60EMU5XRoKG7vIJosQ/aNZFeG2GUYBPxTFqEfCDuv1uH0CODmuSxuj6LkTY46g\nPgMZV8iccc/tdrU+o4j7ymFX+4ITBV9YWjlyL+aHp7jHhseshNnIgk7cNoTLjkYfcb9nI8cdZd18\nGGtjvjAuzfz7wesURyPwuHxxDOa5h5fPXDdPsDjz5qShUKB38tNxB8/z5kZ1xCiAH4wqQgRMHTzU\ndOLEeKrn2KJwr6ubWQaVnBaGR9u5YG7MaHN6s38jIl/uzefMUQhX9pkoTfMiUv5IttajOyX/IGo1\nPbVfWOD1HwqWfxgTD/vSPIaURl/UOrl7l0VsnWD5/r6KsE6mTpT+KCMY2YkLi8xIwLdx+aCwsbMX\nWDTHusmRaSZrGEzzPf/ENj6exrR1+wHVQLyflvk9kO3pHoNRCW+9L5G6QmU2vv36D/g6X0WRI+K8\nsZ06HV4zf+Izl4u07S22wQ1Zqw9h7ihJgIXDAy2Tm4tuyTOJiFJpn6+R2oPTa8zaCqGNVyoixDvR\n9U8hFj4nkiS2T5YfHT8Pi1K5TKfPX6KzwCJzgUKw0/RlvjsUtisR0dIys1lwDIxljVmGNdLKKo+V\n3W39fvnE1ReJiKgV6Av98PVXiYgolqAyk8MDff4DTqtD0JJmk8fjCKIG7+wz07jfVdbI2SfZCyOD\ngBr3b/Na5fxZ/fZaWeTxkMfaZxy7fQhhWbP80dgYhl+wMYjU7s6L3OvNYbK6gDBERJlYzRwCgoQS\nPOd0SxmDO9e4Lu/eVFZ2J+U10d6Av0m/+B+pCPTOLrdpta5jc3VJ6grsgJc6QXstX+C7MmlaKNHf\n8MI4d4wnhYtEO1M/ef7R0aI+Avt72/Qv/8X/Qj4UyrGdfBDJTmXcZ9BHQ7fWAXH0kgzKCAKwhhLR\nugptFsuHSg/WC47hVZZ1LHq2OEZTvQXfYRV+1gTYYEFN5lbkvxTR1/R9XDThFDwTTi3zOPkvnr9Q\npL36Ls87r33z+0VaOXq49aQxcQwGg8FgMBgMBoPBYDAYHgPYJo7BYDAYDAaDwWAwGAwGw2OAh3J+\nyHOfsrREMSm90rknbGwo5fz6O9eIiCiJlRK73hL6ZV4r0oKEKZz72yoOPBgyDalU0nuHGdPJ9vvq\nznR3i2mq5SWmEvamKqC2f/s6EalLCxFR95AplAep0uU7r/A9GdAwayJUdfeuCnMFGadVK0qr2rjM\n7/Frv/mbRdqaiFF1R1rOr/3BN6h3qPTN48APQmq2luncWRUfO3eGhZdXVzaLtLKjzhFS2MWtZ6pl\nqIvo2cWLKnTmiRtVOiPsyff2B+p2cHjI7zaecN1NE6VDjqd8XYLq1ansEyItXShuear1ngutNQqQ\n0i5CdEA3d28WAE3XuVGBDiaNx9pHj4ucmG6LwqKOgTdDX3eiWOA+6BWCu0cp2AnQ8yoidpXFWme7\nu1yPe1MV9BsOuP1ub3P/ffsdFU/rx5zH515UYb+RXA/MVDoccJ3c39Mx4Dsx7CUU1uP+0etqn/GE\nfFkBgdiStFUAbg158ojERj0iP/CpDM8LxHUnAwqpCkwfFRVD8bN+X+oDBBmrIlA8AjfF195nW+RV\nVawvCfk6R6nO93U8TDwWx4zA/vztd/+GiIiuiyAxEdHCErtHLa+saTnF7aff1zJNh9wPxiPtv57Q\nThvLamP3Oky19nK9bnlpYS4N+Dgog0uSc6Fsg6B9vcRlQtE3V989eJ+2uBFUwf1o1OO6QpeFWoNt\nJ4pGhyK63RbBQB/G0t6I2zMBAebJhCm5Cz66k7KdjsElwIkoz7hzSd+a51bk3LSIVCDZuXjxc7UM\nx4VH3G/nufTMuts4ceB516FNEqFCMIo7O9y3W0DJr9Z0fP2o/GZdV2ZdrYhwnH0499p5LOTgluLs\n6Dz3VHRxSGWco8Conz2aMyjP88gL/Bl3MOc6OENXl39DpJzLPSkUpaDkgyl09Hu0joG4CiAl33N0\n8jl15ezejJuZo/WjO9cc4cjiXhQ9To4KAbt5dca9Tf6OboMf7KvHgedxP83zo2OuAaL0zoUEXRjn\niwO7fyHYwxyx43kC0c4tdCRj/d333iv+1pe5NJtz5rkKbq8usAGuFyYjdgWaJ1CNLt+ZjLUc51L/\nqKshkQpSHxceeeR7wcwaqSLzTg/cqTZEsDZNdC548IDtXqul6xW37myD7d7t8HVernVRE1eF6zdu\nFGlRyO08kcAk7bLmsShu3SXo75fWxX3tqqbd3eI5eHFDXXGfefZ5IiKqgq27epoFZf/oT/6oSPur\nb36Ty4FCtiIfgL37UbgpV+s1euHTn6KVleUizfXBBNy+tkRw+/LVZ4u09XVeNwxHut4+2GMXqGFP\n3a6ymPvqMxc1uEqNuE+/9u2/KtLyAd+7KEvWBNYRC+LahqK9zSa3XSvQcbmYtqW8uhY92GKh6oVT\nF4q04Yj7wt6De1pO+bbwoW2d3nQF3MP24HvtJMhzoniazASZKaQRYI4MPLemxWACcj2Of5l7Ugh6\n4OyyV9F7166ya9XtB7pm/KM//DMiIvrUl58kIqKooc9fXOb6xcAaCy1xHYc1TCaBNFKwIZFzE0IX\nXHFFmsbgViR9bQJr9VhsQQqSEBnlMy65x0NGWTaiLMV5TFyiPHVFLY24HseRXjeWiq+MQH5C2mAB\n2icQ181mGaQriPtXF9ztu/KOUcT5lqCdfHnGBNbPziYkQ7ANU3HFhe8qz+O1ax9kFgL57o4j/YY6\n9QJ/s5cCdad89503iIjo9e27RVoYPZytMSaOwWAwGAwGg8FgMBgMBsNjgIdi4qRpQoedPYoC3X1c\nXuBdwvNPnirSrt9iQc/uRE+n6zGfyELUOjocipiRryedu/v8u9fT09xP//3PExHRINL8Jj3e2XSi\nQ/6anr7uDllEagvCf48lbnOqG16UCPuhAcKsZzZ5xzsd6u7fRE7DKot6IvGFn/sMERE9/byerO8O\neAf96//+G0Xa13/3m9Q/OBkTp1lv0c/85M/T0pKybkInQAwiwsmY6yzLNc0T7ooTwiIiCkSgK4Tw\nxLn8PZrqTv90yvkFPRU9W5Ldy17E7X73vtZxf8inOCnkEUjdIUsl+P/Ze7Mgy7LrOmzf6c1Tvpyz\nsrLmqau7qwd0o9ENNEA0QBCkKJKCLJEyFQrRdNgRDjsU4Qj/+c+WHPaPLX+YYQVlSUQwSIikQIIE\nCJDdjaHnuWses6oyK+d8L9/87nz9sfe5e2dXAujKTH6U4uyfyjrvvXvPPcM+55691tok1BkJRFds\n4dGmKVJsRnTGGIqTciViLKOCKsomo+37ln42AQgDeexKxTselopIuErTLeqpdC4z4qTVokjMnaWN\ntOwapSgsTQmkAKFAVBrxlsfX/c4rHwAAwNvvXkrLSiXsW0v0cWUUx+rkwWOiTqov2IaELJBp1DMU\nHbGl8BghcWJxLL2TgOZuzLIsqNRqkEhkSSqwyWUKGSFP+rs9jN5khTCfSusp/UqP0C7rAz7L9km4\n7OBjHAULsxgx6FDEyxat9e5FQh02OEVgnxAmhhCkLRQxIlgsscBlSINpdEyIE9NjbDR4XnVdiuaK\naKCjxIHzImJkm7BXsdEkSSAMfQgEuqRew4hsQaRmnaG0oT2Pyy4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CQqheOMPCygWCF15Y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\nOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c207b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig = plt.figure(figsize=(20,5))\n",
    "for i in range(36):\n",
    "    ax = fig.add_subplot(3, 12, i + 1, xticks=[], yticks=[])\n",
    "    ax.imshow(np.squeeze(x_train[i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 3. Rescale the Images by Dividing Every Pixel in Every Image by 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# rescale [0,255] --> [0,1]\n",
    "x_train = x_train.astype('float32')/255\n",
    "x_test = x_test.astype('float32')/255 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 4.  Break Dataset into Training, Testing, and Validation Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (45000, 32, 32, 3)\n",
      "45000 train samples\n",
      "10000 test samples\n",
      "5000 validation samples\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "\n",
    "# one-hot encode the labels\n",
    "num_classes = len(np.unique(y_train))\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "# break training set into training and validation sets\n",
    "(x_train, x_valid) = x_train[5000:], x_train[:5000]\n",
    "(y_train, y_valid) = y_train[5000:], y_train[:5000]\n",
    "\n",
    "# print shape of training set\n",
    "print('x_train shape:', x_train.shape)\n",
    "\n",
    "# print number of training, validation, and test images\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "print(x_valid.shape[0], 'validation samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 5. Define the Model Architecture "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten_1 (Flatten)          (None, 3072)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1000)              3073000   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 1000)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 512)               512512    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 10)                5130      \n",
      "=================================================================\n",
      "Total params: 3,590,642.0\n",
      "Trainable params: 3,590,642.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "\n",
    "# define the model\n",
    "model = Sequential()\n",
    "model.add(Flatten(input_shape = x_train.shape[1:]))\n",
    "model.add(Dense(1000, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 6. Compile the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# compile the model\n",
    "model.compile(loss='categorical_crossentropy', optimizer='rmsprop', \n",
    "                  metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 7. Train the Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 45000 samples, validate on 5000 samples\n",
      "Epoch 1/20\n",
      "Epoch 00000: val_loss improved from inf to 1.91876, saving model to MLP.weights.best.hdf5\n",
      "52s - loss: 3.2886 - acc: 0.2462 - val_loss: 1.9188 - val_acc: 0.3000\n",
      "Epoch 2/20\n",
      "Epoch 00001: val_loss did not improve\n",
      "54s - loss: 1.8626 - acc: 0.3242 - val_loss: 1.9204 - val_acc: 0.3082\n",
      "Epoch 3/20\n",
      "Epoch 00002: val_loss improved from 1.91876 to 1.78092, saving model to MLP.weights.best.hdf5\n",
      "52s - loss: 1.8230 - acc: 0.3438 - val_loss: 1.7809 - val_acc: 0.3588\n",
      "Epoch 4/20\n",
      "Epoch 00003: val_loss improved from 1.78092 to 1.72077, saving model to MLP.weights.best.hdf5\n",
      "54s - loss: 1.7887 - acc: 0.3575 - val_loss: 1.7208 - val_acc: 0.3640\n",
      "Epoch 5/20\n",
      "Epoch 00004: val_loss did not improve\n",
      "55s - loss: 1.7777 - acc: 0.3651 - val_loss: 1.7357 - val_acc: 0.3500\n",
      "Epoch 6/20\n",
      "Epoch 00005: val_loss improved from 1.72077 to 1.71538, saving model to MLP.weights.best.hdf5\n",
      "53s - loss: 1.7641 - acc: 0.3675 - val_loss: 1.7154 - val_acc: 0.3818\n",
      "Epoch 7/20\n",
      "Epoch 00006: val_loss did not improve\n",
      "52s - loss: 1.7616 - acc: 0.3700 - val_loss: 1.7708 - val_acc: 0.3670\n",
      "Epoch 8/20\n",
      "Epoch 00007: val_loss did not improve\n",
      "52s - loss: 1.7641 - acc: 0.3729 - val_loss: 1.7766 - val_acc: 0.3738\n",
      "Epoch 9/20\n",
      "Epoch 00008: val_loss improved from 1.71538 to 1.70597, saving model to MLP.weights.best.hdf5\n",
      "52s - loss: 1.7709 - acc: 0.3672 - val_loss: 1.7060 - val_acc: 0.3840\n",
      "Epoch 10/20\n",
      "Epoch 00009: val_loss did not improve\n",
      "51s - loss: 1.7635 - acc: 0.3744 - val_loss: 1.8535 - val_acc: 0.3260\n",
      "Epoch 11/20\n",
      "Epoch 00010: val_loss did not improve\n",
      "54s - loss: 1.7551 - acc: 0.3780 - val_loss: 1.7249 - val_acc: 0.3758\n",
      "Epoch 12/20\n",
      "Epoch 00011: val_loss did not improve\n",
      "55s - loss: 1.7617 - acc: 0.3757 - val_loss: 1.7308 - val_acc: 0.3660\n",
      "Epoch 13/20\n",
      "Epoch 00012: val_loss did not improve\n",
      "52s - loss: 1.7694 - acc: 0.3745 - val_loss: 1.9086 - val_acc: 0.3150\n",
      "Epoch 14/20\n",
      "Epoch 00013: val_loss did not improve\n",
      "53s - loss: 1.7654 - acc: 0.3711 - val_loss: 1.7625 - val_acc: 0.3684\n",
      "Epoch 15/20\n",
      "Epoch 00014: val_loss did not improve\n",
      "52s - loss: 1.7691 - acc: 0.3726 - val_loss: 1.7753 - val_acc: 0.3778\n",
      "Epoch 16/20\n",
      "Epoch 00015: val_loss did not improve\n",
      "52s - loss: 1.7780 - acc: 0.3688 - val_loss: 1.7723 - val_acc: 0.3592\n",
      "Epoch 17/20\n",
      "Epoch 00016: val_loss did not improve\n",
      "52s - loss: 1.7757 - acc: 0.3675 - val_loss: 1.7359 - val_acc: 0.3644\n",
      "Epoch 18/20\n",
      "Epoch 00017: val_loss did not improve\n",
      "54s - loss: 1.7868 - acc: 0.3676 - val_loss: 1.7861 - val_acc: 0.3538\n",
      "Epoch 19/20\n",
      "Epoch 00018: val_loss did not improve\n",
      "53s - loss: 1.7797 - acc: 0.3717 - val_loss: 1.7431 - val_acc: 0.3698\n",
      "Epoch 20/20\n",
      "Epoch 00019: val_loss improved from 1.70597 to 1.70173, saving model to MLP.weights.best.hdf5\n",
      "52s - loss: 1.7857 - acc: 0.3670 - val_loss: 1.7017 - val_acc: 0.3926\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x10935a4a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint   \n",
    "\n",
    "# train the model\n",
    "checkpointer = ModelCheckpoint(filepath='MLP.weights.best.hdf5', verbose=1, \n",
    "                               save_best_only=True)\n",
    "hist = model.fit(x_train, y_train, batch_size=32, epochs=20,\n",
    "          validation_data=(x_valid, y_valid), callbacks=[checkpointer], \n",
    "          verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 8. Load the Model with the Best Classification Accuracy on the Validation Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# load the weights that yielded the best validation accuracy\n",
    "model.load_weights('MLP.weights.best.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 9. Calculate Classification Accuracy on Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Test accuracy: 0.4\n"
     ]
    }
   ],
   "source": [
    "# evaluate and print test accuracy\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('\\n', 'Test accuracy:', score[1])"
   ]
  }
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